Modeling of Exhaust Gas Temperature at the Turbine Outlet Using Neural Networks and a Physical Expansion Model

The accurate estimation of exhaust gas temperature across the turbine is always more important for the optimization of engine performance, ensuring durability of the turbine impeller and catalyst, and reducing and calculating emissions concentration. Traditional physical modeling approaches, based on thermodynamic and fluid dynamics features of gas expansion, can be used for this purpose. However, recent advancements in machine learning, particularly neural networks, offer a data-driven alternative that may enhance prediction accuracy and computational efficiency. This study presents a methodology that integrates a semi-physical turbine model for estimating the exhaust gas temperature at the turbine outlet with a neural network-based approach for predicting the pressure at the turbine inlet. The model utilizes the exhaust gas temperature upstream of the turbine, a model for which was developed in a previous work of the authors. The models are calibrated with steady-state data and then evaluated based on accuracy and robustness under transient operating conditions on six driving cycles with different features. In this way, robust and reliable validation of the models is presented, since the testing is performed on various conditions not used for model development and calibration. Results show an average root mean square error of 14%, including the initial portions of driving cycles performed with a cold engine. Thus, the developed approach that includes multiple modeling methods shows a good predictivity, which is the main objective of this research activity.